Next Generation Algorithms for Planning Production and Inventories with Uncertain Demand and Congestion

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This award provides funding for the development of comprehensive, computationally tractable and user-friendly production planning algorithms that consider uncertain demand, using the tools of stochastic optimization, and the queuing behavior that characterizes production resources with limited capacity, characterized by nonlinear clearing functions. In today's global supply chains, effective coordination of operations across space and time is vital to capital-intensive industries like semiconductor manufacturing where global supply chains, short product life cycles and rapidly changing market conditions render effective supply chain coordination critical. However, despite the fact that problems related to the planning of production and inventories have been the stock in trade of industrial engineering and operations research for the last five decades, a comprehensive solution to the problem as faced in industry is still unavailable. The specific aim of this project is to develop both exact solution methods as well as approximate solutions for larger problems whose solution quality can by characterized either through analytical bounds or by systematic computational experiments. The test bed for this project will be the semiconductor industry, whose global supply chains, short product life cycles and rapidly changing market conditions render effective supply chain coordination critical.If successful, the impact of this work on U.S. industrial competitiveness will be significant. Company-wide planning models supporting supply chain coordination have repeatedly been shown to yield significant benefits, and the integration of concepts that have previously been studied separately in the domains of mathematical programming and stochastic inventory modeling will provide an avenue for significant new developments in production planning. In terms of education there are also significant benefits. Most IE/OR curricula present deterministic and stochastic modeling as two completely distinct course sequences, with the result that students view the two technologies as unrelated. The work developed in this research will be taught at the undergraduate level, helping students develop a deeper understanding of the connection between deterministic and stochastic models.